amharic-hate-speech
This model is a fine-tuned version of uhhlt/am-roberta on an AmahricHateSpeechRANL dataset. It achieves the following results on the evaluation set:
- Loss: 0.6437
- Accuracy: 0.7373
- Precision: 0.7216
- Recall: 0.7149
- F1: 0.7180
How to use it
from transformers import pipeline
amhate_classifier = pipeline("text-classification", model="uhhlt/amharic-hate-speech")
amhate_classifier(["π³βοΈ π³βοΈααα
αα
α¨αα°ααα ααα α°α»ααͺ α’αα¨α΅α΅ααα΅ !!!π³βοΈ π³βοΈα’",
"α αα° α αα αα α¨ααα
αα
? αα‘α α αααͺ α¨ααα α«α΅ααα’ α°αα α«αα αα‘α α₯α¨αα° ααα ααα α«αα αααα α€αααα΅ α ααα΅αα α’ αα 100% α«αΈααα",
"α α αα΅α
α°α°α¨α α£αα³ α°αααͺ"])
Output
[{'label': 'normal', 'score': 0.8840981721878052},
{'label': 'hate', 'score': 0.519339382648468},
{'label': 'hate', 'score': 0.9630571007728577}]
Model description
More information needed
Intended uses & limitations
More information needed
Training and evaluation data
More information needed
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 128
- eval_batch_size: 128
- seed: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 8
Training results
Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 |
---|---|---|---|---|---|---|---|
0.8441 | 1.0 | 94 | 0.6699 | 0.7053 | 0.6913 | 0.6640 | 0.6737 |
0.6199 | 2.0 | 188 | 0.6505 | 0.72 | 0.7060 | 0.6995 | 0.6994 |
0.5295 | 3.0 | 282 | 0.6240 | 0.736 | 0.7201 | 0.7125 | 0.7159 |
0.4614 | 4.0 | 376 | 0.6437 | 0.7373 | 0.7216 | 0.7149 | 0.7180 |
0.3955 | 5.0 | 470 | 0.6922 | 0.7207 | 0.7001 | 0.7072 | 0.7031 |
0.3529 | 6.0 | 564 | 0.6995 | 0.7247 | 0.7050 | 0.7029 | 0.7039 |
0.3076 | 7.0 | 658 | 0.7352 | 0.7253 | 0.7067 | 0.7000 | 0.7031 |
0.2863 | 8.0 | 752 | 0.7470 | 0.7227 | 0.7019 | 0.6983 | 0.7000 |
Framework versions
- Transformers 4.32.1
- Pytorch 2.0.1+cu117
- Datasets 2.14.4
- Tokenizers 0.13.3
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